Correctly ascertaining a user's intent underlying a search query is critical to effectively satisfying the user's information need. In some cases, intent can be directly inferred from terms in a query. For example, a user will often be explicit about the fact that a query is location-sensitive by including geographic terms in the query, e.g., “disneyland orlando.” Existing approaches to identifying location-sensitive queries focus on recognizing such terms (e.g., using name entity recognition (NER) techniques in combination with the Gazetteer geographic location ontology) and location disambiguation (e.g., using natural language processing (NLP) and machine learning techniques). However, most of the work to date ignores the fact that location information may be implied by a large number of terms that are not recognizable as geographic terms. For example, the term “disneyland” is not, itself, a geographic location, but it implies multiple geographic locations, e.g., Anaheim, Calif., and Orlando, Fla. As a result, many location-sensitive queries are not recognized by existing search engines.
Moreover, many documents (e.g., web pages) that can be accessed by search engines are similarly ambiguous with regard to whether and to what extent they relate to particular locations. Even in the cases in which documents might have tags or metadata that identify a location, there may be other locations relevant to those documents that are not identified. So, even where a user's intent is understood to relate to a geographical location, many relevant results might still be missed or improperly ranked because they are not clearly identified as relating to particular locations.